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e655301 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | -- Migration: Switch from 1536-dim to 384-dim embeddings (MiniLM)
-- Run this in Supabase SQL Editor if you already have the chunks table with vector(1536)
-- 1. Drop the ivfflat index (required before altering column)
drop index if exists idx_chunks_embedding;
-- 2. Clear existing chunks (old 1536-dim embeddings are incompatible)
truncate table chunks;
-- 3. Replace embedding column with 384-dim version
alter table chunks drop column embedding;
alter table chunks add column embedding vector(384);
-- 4. Recreate the ivfflat index (run AFTER ingesting new PDF/TXT - requires rows)
-- create index if not exists idx_chunks_embedding on chunks using ivfflat (embedding vector_cosine_ops) with (lists = 100);
-- 5. Update match_chunks RPC
create or replace function match_chunks(
query_embedding vector(384),
match_count int,
p_notebook_id uuid
)
returns table (id uuid, content text, metadata jsonb, similarity float)
language plpgsql as $$
begin
return query
select c.id, c.content, c.metadata,
1 - (c.embedding <=> query_embedding) as similarity
from chunks c
where c.notebook_id = p_notebook_id
and c.embedding is not null
order by c.embedding <=> query_embedding
limit match_count;
end;
$$;
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